Overview

Dataset statistics

Number of variables12
Number of observations65281
Missing cells0
Missing cells (%)0.0%
Duplicate rows3657
Duplicate rows (%)5.6%
Total size in memory6.5 MiB
Average record size in memory104.0 B

Variable types

Numeric10
Text1
Categorical1

Alerts

Dataset has 3657 (5.6%) duplicate rowsDuplicates
Discount Amount is highly overall correlated with Sales Amount and 3 other fieldsHigh correlation
List Price is highly overall correlated with Sales Price and 1 other fieldsHigh correlation
Sales Amount is highly overall correlated with Discount Amount and 3 other fieldsHigh correlation
Sales Amount Based on List Price is highly overall correlated with Discount Amount and 3 other fieldsHigh correlation
Sales Cost Amount is highly overall correlated with Discount Amount and 3 other fieldsHigh correlation
Sales Margin Amount is highly overall correlated with Discount Amount and 3 other fieldsHigh correlation
Sales Price is highly overall correlated with List Price and 1 other fieldsHigh correlation
Sales Quantity is highly overall correlated with List Price and 1 other fieldsHigh correlation
Sales Cost Amount is highly skewed (γ1 = 21.01078573)Skewed
Sales Quantity is highly skewed (γ1 = 23.00739577)Skewed
Discount Amount has 1215 (1.9%) zerosZeros

Reproduction

Analysis started2023-07-09 06:30:40.850514
Analysis finished2023-07-09 06:30:55.817760
Duration14.97 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

CustKey
Real number (ℝ)

Distinct615
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10017703
Minimum10000453
Maximum10027583
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1020.0 KiB
2023-07-09T12:00:55.923813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10000453
5-th percentile10002506
Q110012715
median10019665
Q310023511
95-th percentile10026006
Maximum10027583
Range27130
Interquartile range (IQR)10796

Descriptive statistics

Standard deviation7176.1904
Coefficient of variation (CV)0.0007163509
Kurtosis-0.37137474
Mean10017703
Median Absolute Deviation (MAD)4886
Skewness-0.77019433
Sum6.5396565 × 1011
Variance51497708
MonotonicityNot monotonic
2023-07-09T12:00:56.067179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10025919 2760
 
4.2%
10019194 2752
 
4.2%
10012715 1431
 
2.2%
10012226 1389
 
2.1%
10025025 1143
 
1.8%
10023524 1042
 
1.6%
10020515 1010
 
1.5%
10017638 792
 
1.2%
10022456 741
 
1.1%
10002506 714
 
1.1%
Other values (605) 51507
78.9%
ValueCountFrequency (%)
10000453 329
0.5%
10000455 19
 
< 0.1%
10000456 104
 
0.2%
10000457 19
 
< 0.1%
10000458 10
 
< 0.1%
10000460 120
 
0.2%
10000461 251
0.4%
10000462 3
 
< 0.1%
10000466 123
 
0.2%
10000469 162
0.2%
ValueCountFrequency (%)
10027583 25
 
< 0.1%
10027575 5
 
< 0.1%
10027572 52
 
0.1%
10027560 42
 
0.1%
10027381 108
0.2%
10027370 235
0.4%
10027356 21
 
< 0.1%
10027348 14
 
< 0.1%
10027340 35
 
0.1%
10027119 176
0.3%

Discount Amount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17820
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1855.5464
Minimum-255820.8
Maximum343532.66
Zeros1215
Zeros (%)1.9%
Negative972
Negative (%)1.5%
Memory size1020.0 KiB
2023-07-09T12:00:56.203020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-255820.8
5-th percentile18.68
Q1246.03
median441.76
Q3999.76
95-th percentile6353
Maximum343532.66
Range599353.46
Interquartile range (IQR)753.73

Descriptive statistics

Standard deviation9037.0746
Coefficient of variation (CV)4.8703037
Kurtosis379.74186
Mean1855.5464
Median Absolute Deviation (MAD)233.95
Skewness10.84186
Sum1.2113193 × 108
Variance81668717
MonotonicityNot monotonic
2023-07-09T12:00:56.327629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1215
 
1.9%
24.88 103
 
0.2%
606.84 100
 
0.2%
639.82 97
 
0.1%
601.9033 93
 
0.1%
402.7 93
 
0.1%
634.6133 93
 
0.1%
918.1412 88
 
0.1%
169.36 87
 
0.1%
385.98 87
 
0.1%
Other values (17810) 63225
96.9%
ValueCountFrequency (%)
-255820.8 1
 
< 0.1%
-245587.97 1
 
< 0.1%
-238792.73 1
 
< 0.1%
-231837.6 3
< 0.1%
-222564.1 3
< 0.1%
-127176 1
 
< 0.1%
-122088.96 1
 
< 0.1%
-84573.72 1
 
< 0.1%
-81190.77 1
 
< 0.1%
-53626 1
 
< 0.1%
ValueCountFrequency (%)
343532.66 2
< 0.1%
339103.35 1
 
< 0.1%
331487.76 2
< 0.1%
327213.75 1
 
< 0.1%
322454.09 1
 
< 0.1%
210371 4
< 0.1%
202995 4
< 0.1%
191196.5532 2
< 0.1%
189333.9 1
 
< 0.1%
182832.8832 2
< 0.1%

Item
Text

Distinct657
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1020.0 KiB
2023-07-09T12:00:56.559528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length37
Median length32
Mean length21.722278
Min length8

Characters and Unicode

Total characters1418052
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)< 0.1%

Sample

1st rowUrban Large Eggs
2nd rowMoms Sliced Turkey
3rd rowCutting Edge Foot-Long Hot Dogs
4th rowKiwi Lox
5th rowHigh Top Sweet Onion
ValueCountFrequency (%)
canned 6378
 
2.8%
ebony 5460
 
2.4%
cheese 5194
 
2.3%
better 4570
 
2.0%
red 4271
 
1.9%
top 4173
 
1.8%
spade 4161
 
1.8%
high 4138
 
1.8%
best 3480
 
1.5%
nationeel 3328
 
1.4%
Other values (294) 184534
80.3%
2023-07-09T12:00:56.931750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
164406
 
11.6%
e 147096
 
10.4%
o 92467
 
6.5%
a 92276
 
6.5%
n 74074
 
5.2%
i 69460
 
4.9%
t 68733
 
4.8%
r 67352
 
4.7%
l 59835
 
4.2%
s 57796
 
4.1%
Other values (46) 524557
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1013747
71.5%
Uppercase Letter 236243
 
16.7%
Space Separator 164406
 
11.6%
Dash Punctuation 2160
 
0.2%
Other Punctuation 748
 
0.1%
Decimal Number 748
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 147096
14.5%
o 92467
 
9.1%
a 92276
 
9.1%
n 74074
 
7.3%
i 69460
 
6.9%
t 68733
 
6.8%
r 67352
 
6.6%
l 59835
 
5.9%
s 57796
 
5.7%
d 40545
 
4.0%
Other values (16) 244113
24.1%
Uppercase Letter
ValueCountFrequency (%)
B 31428
13.3%
C 30873
13.1%
S 26130
11.1%
T 19374
 
8.2%
F 16239
 
6.9%
M 12522
 
5.3%
P 11469
 
4.9%
L 11022
 
4.7%
D 10754
 
4.6%
E 10237
 
4.3%
Other values (15) 56195
23.8%
Decimal Number
ValueCountFrequency (%)
1 579
77.4%
2 169
 
22.6%
Space Separator
ValueCountFrequency (%)
164406
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2160
100.0%
Other Punctuation
ValueCountFrequency (%)
% 748
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1249990
88.1%
Common 168062
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 147096
 
11.8%
o 92467
 
7.4%
a 92276
 
7.4%
n 74074
 
5.9%
i 69460
 
5.6%
t 68733
 
5.5%
r 67352
 
5.4%
l 59835
 
4.8%
s 57796
 
4.6%
d 40545
 
3.2%
Other values (41) 480356
38.4%
Common
ValueCountFrequency (%)
164406
97.8%
- 2160
 
1.3%
% 748
 
0.4%
1 579
 
0.3%
2 169
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1418052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
164406
 
11.6%
e 147096
 
10.4%
o 92467
 
6.5%
a 92276
 
6.5%
n 74074
 
5.2%
i 69460
 
4.9%
t 68733
 
4.8%
r 67352
 
4.7%
l 59835
 
4.2%
s 57796
 
4.1%
Other values (46) 524557
37.0%

List Price
Real number (ℝ)

HIGH CORRELATION 

Distinct1062
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean514.70126
Minimum0
Maximum2760.7
Zeros295
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1020.0 KiB
2023-07-09T12:00:57.078222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36.69
Q1181.56
median325.19
Q3803.86
95-th percentile1431.23
Maximum2760.7
Range2760.7
Interquartile range (IQR)622.3

Descriptive statistics

Standard deviation449.18811
Coefficient of variation (CV)0.87271615
Kurtosis0.012496642
Mean514.70126
Median Absolute Deviation (MAD)217.35
Skewness1.0054596
Sum33600213
Variance201769.95
MonotonicityNot monotonic
2023-07-09T12:00:57.200313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
298 1508
 
2.3%
1431.23 1426
 
2.2%
966.44 1192
 
1.8%
1275.1 1126
 
1.7%
192.34 1041
 
1.6%
1627.84 1035
 
1.6%
157.76 988
 
1.5%
1084.61 975
 
1.5%
181.44 893
 
1.4%
412.03 892
 
1.4%
Other values (1052) 54205
83.0%
ValueCountFrequency (%)
0 295
0.5%
0.3929 150
0.2%
0.4 21
 
< 0.1%
0.405 25
 
< 0.1%
0.41 10
 
< 0.1%
0.445 6
 
< 0.1%
0.52 1
 
< 0.1%
0.61 4
 
< 0.1%
1.6236 2
 
< 0.1%
1.8711 9
 
< 0.1%
ValueCountFrequency (%)
2760.7 12
 
< 0.1%
2291.4 7
 
< 0.1%
2267 10
 
< 0.1%
1975 113
0.2%
1920 61
0.1%
1880 19
 
< 0.1%
1759.4 45
 
0.1%
1731.4 35
 
0.1%
1691.4 12
 
< 0.1%
1688.13 150
0.2%

Sales Amount
Real number (ℝ)

HIGH CORRELATION 

Distinct17895
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2852.0055
Minimum200.01
Maximum555376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1020.0 KiB
2023-07-09T12:00:57.332232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum200.01
5-th percentile215.78
Q1308.38
median553.94
Q31279.96
95-th percentile8777.79
Maximum555376
Range555175.99
Interquartile range (IQR)971.58

Descriptive statistics

Standard deviation15164.456
Coefficient of variation (CV)5.3171202
Kurtosis478.90724
Mean2852.0055
Median Absolute Deviation (MAD)292.92
Skewness18.578552
Sum1.8618177 × 108
Variance2.2996072 × 108
MonotonicityNot monotonic
2023-07-09T12:00:57.462352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
784.97 115
 
0.2%
817.68 115
 
0.2%
294.72 110
 
0.2%
307 104
 
0.2%
597.14 102
 
0.2%
622.02 101
 
0.2%
824.39 100
 
0.2%
791.41 99
 
0.2%
401.16 95
 
0.1%
204.66 92
 
0.1%
Other values (17885) 64248
98.4%
ValueCountFrequency (%)
200.01 6
< 0.1%
200.06 6
< 0.1%
200.08 1
 
< 0.1%
200.14 3
< 0.1%
200.15 5
< 0.1%
200.19 7
< 0.1%
200.21 1
 
< 0.1%
200.3 3
< 0.1%
200.36 1
 
< 0.1%
200.37 6
< 0.1%
ValueCountFrequency (%)
555376 1
 
< 0.1%
539200 5
< 0.1%
517632 5
< 0.1%
472069.6 2
 
< 0.1%
458320 5
< 0.1%
439987.2 5
< 0.1%
310156.07 1
 
< 0.1%
301122.4 2
 
< 0.1%
297240 1
 
< 0.1%
289077.5 2
 
< 0.1%

Sales Amount Based on List Price
Real number (ℝ)

HIGH CORRELATION 

Distinct4060
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4707.5457
Minimum0
Maximum632610.16
Zeros295
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1020.0 KiB
2023-07-09T12:00:57.599547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile390
Q1561.04
median998.16
Q32315.04
95-th percentile16425.12
Maximum632610.16
Range632610.16
Interquartile range (IQR)1754

Descriptive statistics

Standard deviation20696.594
Coefficient of variation (CV)4.3964722
Kurtosis278.71143
Mean4707.5457
Median Absolute Deviation (MAD)524.88
Skewness14.074724
Sum3.0731329 × 108
Variance4.2834901 × 108
MonotonicityNot monotonic
2023-07-09T12:00:57.731257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1431.23 590
 
0.9%
1627.84 530
 
0.8%
803.86 498
 
0.8%
596 448
 
0.7%
1254.1899 418
 
0.6%
966.44 376
 
0.6%
439.7 372
 
0.6%
507.75 363
 
0.6%
767.75 348
 
0.5%
939.57 343
 
0.5%
Other values (4050) 60995
93.4%
ValueCountFrequency (%)
0 295
0.5%
194 2
 
< 0.1%
195.61 1
 
< 0.1%
198.396 1
 
< 0.1%
198.63 1
 
< 0.1%
200.7 8
 
< 0.1%
200.8 1
 
< 0.1%
201.69 3
 
< 0.1%
202.14 1
 
< 0.1%
202.6 1
 
< 0.1%
ValueCountFrequency (%)
632610.16 5
< 0.1%
624453.75 2
 
< 0.1%
539200 11
< 0.1%
458320 12
< 0.1%
391924.7232 5
< 0.1%
387395 8
< 0.1%
348655.5 2
 
< 0.1%
332196.405 2
 
< 0.1%
330708.3792 5
< 0.1%
310273.7392 2
 
< 0.1%

Sales Cost Amount
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5513
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1661.0047
Minimum0
Maximum366576
Zeros348
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1020.0 KiB
2023-07-09T12:00:57.871185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile85.36
Q1167.79
median304.5
Q3687.4
95-th percentile4946.11
Maximum366576
Range366576
Interquartile range (IQR)519.61

Descriptive statistics

Standard deviation9556.5562
Coefficient of variation (CV)5.7534794
Kurtosis614.26709
Mean1661.0047
Median Absolute Deviation (MAD)171.25
Skewness21.010786
Sum1.0843205 × 108
Variance91327767
MonotonicityNot monotonic
2023-07-09T12:00:58.007896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
449.69 534
 
0.8%
475.75 457
 
0.7%
0 348
 
0.5%
134.67 305
 
0.5%
162.89 289
 
0.4%
205.72 253
 
0.4%
159.14 242
 
0.4%
16718.08 234
 
0.4%
546.44 231
 
0.4%
344.28 229
 
0.4%
Other values (5503) 62159
95.2%
ValueCountFrequency (%)
0 348
0.5%
12.97 2
 
< 0.1%
19.55 4
 
< 0.1%
20.8 6
 
< 0.1%
26 1
 
< 0.1%
31.19 4
 
< 0.1%
33.97 3
 
< 0.1%
35.48 2
 
< 0.1%
35.54 5
 
< 0.1%
36.03 1
 
< 0.1%
ValueCountFrequency (%)
366576 7
 
< 0.1%
353292.8 4
 
< 0.1%
311589.6 12
 
< 0.1%
185048.85 2
 
< 0.1%
161446.35 5
 
< 0.1%
157412.85 2
 
< 0.1%
153635.03 5
 
< 0.1%
146630.4 4
 
< 0.1%
141265.56 36
0.1%
137736.24 4
 
< 0.1%

Sales Margin Amount
Real number (ℝ)

HIGH CORRELATION 

Distinct21295
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1191.0008
Minimum-3932.93
Maximum188800
Zeros3
Zeros (%)< 0.1%
Negative576
Negative (%)0.9%
Memory size1020.0 KiB
2023-07-09T12:00:58.151584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3932.93
5-th percentile61.54
Q1129.95
median246.49
Q3579.32
95-th percentile3824.43
Maximum188800
Range192732.93
Interquartile range (IQR)449.37

Descriptive statistics

Standard deviation5860.8134
Coefficient of variation (CV)4.9209148
Kurtosis324.93262
Mean1191.0008
Median Absolute Deviation (MAD)140.28
Skewness15.571533
Sum77749723
Variance34349134
MonotonicityNot monotonic
2023-07-09T12:00:58.283843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
374.7 93
 
0.1%
5317.17 88
 
0.1%
6235.31 87
 
0.1%
341.72 84
 
0.1%
15.32 69
 
0.1%
37.08 67
 
0.1%
52.8 67
 
0.1%
431.88 64
 
0.1%
464.59 64
 
0.1%
24.53 63
 
0.1%
Other values (21285) 64535
98.9%
ValueCountFrequency (%)
-3932.93 1
< 0.1%
-3764.4 2
< 0.1%
-3673.68 2
< 0.1%
-3608.81 1
< 0.1%
-3414.01 2
< 0.1%
-3132.65 2
< 0.1%
-2533.97 2
< 0.1%
-2508.21 2
< 0.1%
-2488.89 1
< 0.1%
-2103.04 2
< 0.1%
ValueCountFrequency (%)
188800 1
 
< 0.1%
185907.2 2
< 0.1%
172624 3
< 0.1%
164339.2 2
< 0.1%
160480 2
< 0.1%
156773.4 1
 
< 0.1%
156521.04 1
 
< 0.1%
151056 3
< 0.1%
148401.6 3
< 0.1%
147487.37 2
< 0.1%

Sales Price
Real number (ℝ)

HIGH CORRELATION 

Distinct14789
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.69251
Minimum0
Maximum6035
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1020.0 KiB
2023-07-09T12:00:58.426499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.425556
Q1100.07
median183.7575
Q3448.22
95-th percentile789.66
Maximum6035
Range6035
Interquartile range (IQR)348.15

Descriptive statistics

Standard deviation252.03218
Coefficient of variation (CV)0.88839913
Kurtosis6.8824229
Mean283.69251
Median Absolute Deviation (MAD)116.4725
Skewness1.4189747
Sum18519730
Variance63520.217
MonotonicityNot monotonic
2023-07-09T12:00:58.555730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140.43 191
 
0.3%
817.68 189
 
0.3%
133.41 181
 
0.3%
783.17 138
 
0.2%
824.39 138
 
0.2%
23.47 136
 
0.2%
82.87333333 125
 
0.2%
221.04 120
 
0.2%
230.25 120
 
0.2%
230.98 120
 
0.2%
Other values (14779) 63823
97.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.3373411765 2
 
< 0.1%
0.3514 1
 
< 0.1%
0.3619411765 1
 
< 0.1%
0.37718 67
0.1%
0.384 9
 
< 0.1%
0.3888 12
 
< 0.1%
0.3929 67
0.1%
0.3936 5
 
< 0.1%
0.4 9
 
< 0.1%
ValueCountFrequency (%)
6035 1
< 0.1%
3748 2
< 0.1%
3233.36 1
< 0.1%
3009.86 1
< 0.1%
3003.41 1
< 0.1%
2823 1
< 0.1%
2753.32 1
< 0.1%
2560 1
< 0.1%
2540.17 1
< 0.1%
2360.1 1
< 0.1%

Sales Quantity
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct280
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.085017
Minimum0
Maximum16000
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1020.0 KiB
2023-07-09T12:00:58.694473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q38
95-th percentile86
Maximum16000
Range16000
Interquartile range (IQR)6

Descriptive statistics

Standard deviation429.66505
Coefficient of variation (CV)9.5301072
Kurtosis649.74753
Mean45.085017
Median Absolute Deviation (MAD)2
Skewness23.007396
Sum2943195
Variance184612.05
MonotonicityNot monotonic
2023-07-09T12:00:58.832065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15264
23.4%
2 13466
20.6%
3 7056
10.8%
4 4973
 
7.6%
5 3519
 
5.4%
6 3061
 
4.7%
10 2596
 
4.0%
8 1460
 
2.2%
12 1314
 
2.0%
20 1034
 
1.6%
Other values (270) 11538
17.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 15264
23.4%
2 13466
20.6%
3 7056
10.8%
4 4973
 
7.6%
5 3519
 
5.4%
6 3061
 
4.7%
7 711
 
1.1%
8 1460
 
2.2%
9 453
 
0.7%
ValueCountFrequency (%)
16000 11
 
< 0.1%
13600 12
 
< 0.1%
9504 7
 
< 0.1%
8316 40
0.1%
7128 21
< 0.1%
7126 2
 
< 0.1%
6480 2
 
< 0.1%
6400 4
 
< 0.1%
5834 4
 
< 0.1%
4752 13
 
< 0.1%

Year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1020.0 KiB
2017
30574 
2019
28021 
2018
6686 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters261124
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 30574
46.8%
2019 28021
42.9%
2018 6686
 
10.2%

Length

2023-07-09T12:00:58.965277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T12:00:59.099626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2017 30574
46.8%
2019 28021
42.9%
2018 6686
 
10.2%

Most occurring characters

ValueCountFrequency (%)
2 65281
25.0%
0 65281
25.0%
1 65281
25.0%
7 30574
11.7%
9 28021
10.7%
8 6686
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 261124
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 65281
25.0%
0 65281
25.0%
1 65281
25.0%
7 30574
11.7%
9 28021
10.7%
8 6686
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 261124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 65281
25.0%
0 65281
25.0%
1 65281
25.0%
7 30574
11.7%
9 28021
10.7%
8 6686
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 261124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 65281
25.0%
0 65281
25.0%
1 65281
25.0%
7 30574
11.7%
9 28021
10.7%
8 6686
 
2.6%

Month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3070265
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1020.0 KiB
2023-07-09T12:00:59.200710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5635367
Coefficient of variation (CV)0.56501058
Kurtosis-1.3040676
Mean6.3070265
Median Absolute Deviation (MAD)3
Skewness0.076473773
Sum411729
Variance12.698794
MonotonicityNot monotonic
2023-07-09T12:00:59.302683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 7308
11.2%
2 6556
10.0%
1 6066
9.3%
12 5645
8.6%
9 5555
8.5%
6 5376
8.2%
10 5250
8.0%
11 5247
8.0%
5 5167
7.9%
8 4738
7.3%
Other values (2) 8373
12.8%
ValueCountFrequency (%)
1 6066
9.3%
2 6556
10.0%
3 7308
11.2%
4 3977
6.1%
5 5167
7.9%
6 5376
8.2%
7 4396
6.7%
8 4738
7.3%
9 5555
8.5%
10 5250
8.0%
ValueCountFrequency (%)
12 5645
8.6%
11 5247
8.0%
10 5250
8.0%
9 5555
8.5%
8 4738
7.3%
7 4396
6.7%
6 5376
8.2%
5 5167
7.9%
4 3977
6.1%
3 7308
11.2%

Interactions

2023-07-09T12:00:54.126605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:42.511688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:43.750896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:44.954894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:46.144593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:47.349291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:48.603464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:50.372631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:51.636700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:52.852371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:54.246022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:42.640990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:43.869174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:45.073118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:46.266163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:47.473152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:48.726072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:50.497248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:51.759360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:52.976547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:54.360506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:42.761387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:43.982944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:45.190067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:46.378676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:47.592120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:48.849156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:50.619090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:51.878931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:53.101677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:54.474050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:42.879472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:44.097355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:45.304250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:46.492246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:47.726636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:48.968284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:50.739249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:51.997129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:53.226096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:54.588119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:42.997804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:44.212671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:45.417963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:46.605363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:47.843797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:49.608125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:50.859521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:52.114968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:53.349354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:54.715985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:43.123374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:44.333477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:45.539514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:46.728209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:47.967353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:49.735413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:50.988612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:52.239250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:53.480165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:54.841484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:43.253718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:44.457646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:45.663351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:46.852295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:48.094708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:49.864469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:51.130574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:52.364243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:53.613352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:54.965952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:43.383357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:44.583229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:45.787258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:46.985527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:48.228295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:49.994384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:51.258255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:52.490098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:53.746275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:55.085191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:43.504174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:44.707705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:45.905074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:47.109455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:48.352401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:50.118562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:51.381925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:52.608684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:53.872295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:55.215877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:43.635376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:44.838880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:46.033039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:47.235778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:48.485059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:50.251418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:51.517716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:52.736677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T12:00:54.003899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-09T12:00:59.418820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
CustKeyDiscount AmountList PriceSales AmountSales Amount Based on List PriceSales Cost AmountSales Margin AmountSales PriceSales QuantityMonthYear
CustKey1.000-0.016-0.0080.0150.0060.0190.011-0.0040.0110.0080.171
Discount Amount-0.0161.0000.4380.8230.8960.7830.7310.3670.3800.0420.000
List Price-0.0080.4381.0000.3560.3830.3890.2850.977-0.542-0.0050.005
Sales Amount0.0150.8230.3561.0000.9670.9300.8900.3580.4840.0090.011
Sales Amount Based on List Price0.0060.8960.3830.9671.0000.9130.8590.3460.4760.0230.000
Sales Cost Amount0.0190.7830.3890.9300.9131.0000.7060.3730.4230.0130.000
Sales Margin Amount0.0110.7310.2850.8900.8590.7061.0000.3000.4740.0050.012
Sales Price-0.0040.3670.9770.3580.3460.3730.3001.000-0.567-0.0180.024
Sales Quantity0.0110.380-0.5420.4840.4760.4230.474-0.5671.0000.0250.000
Month0.0080.042-0.0050.0090.0230.0130.005-0.0180.0251.0000.366
Year0.1710.0000.0050.0110.0000.0000.0120.0240.0000.3661.000

Missing values

2023-07-09T12:00:55.376069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-09T12:00:55.618285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CustKeyDiscount AmountItemList PriceSales AmountSales Amount Based on List PriceSales Cost AmountSales Margin AmountSales PriceSales QuantityYearMonth
010000481-237.910Urban Large Eggs0.000237.910.0000.0237.91237.910000120174
110002220368.790Moms Sliced Turkey824.960456.17824.9600.0456.17456.170000120177
210002220109.730Cutting Edge Foot-Long Hot Dogs548.660438.93548.6600.0438.93438.9300001201710
310002489-211.750Kiwi Lox0.000211.750.0000.0211.75211.750000120176
41000451696627.940High Top Sweet Onion408.52089248.66185876.6000.089248.66196.15090145520175
510004516-1950.000Best Choice Fudge Brownies0.0001950.000.0000.01950.001950.000000120175
610007866371.014Moms Sliced Turkey795.314424.30795.3140.0424.30424.300000120179
710009356608.080Tell Tale Garlic575.000541.921150.0000.0541.92270.960000220176
810009356424.800High Top Walnuts51.880353.40778.2000.0353.4023.5600001520176
91000935613492.800Big Time Frozen Cheese Pizza412.03011229.0024721.8000.011229.00187.1500006020176
CustKeyDiscount AmountItemList PriceSales AmountSales Amount Based on List PriceSales Cost AmountSales Margin AmountSales PriceSales QuantityYearMonth
6527210017638596.32Blue Label Canned Beets634.11671.901268.22544.11127.79335.950000220183
65273100176384654.76Moms Sliced Turkey824.965244.769899.523058.482186.28437.0633331220183
65274100176381582.78Gorilla Strawberry Yogurt187.011783.403366.18768.231015.1799.0777781820183
65275100176381095.70Gorilla Jack Cheese1103.001110.302206.00844.55265.75555.150000220183
6527610017638277.61Blue Label Fancy Canned Oysters14.76312.79590.40268.4044.397.8197504020183
6527710017638505.78High Top Oranges119.52569.901075.68239.95329.9563.322222920183
6527810017638410.75Landslide White Sugar436.78462.81873.56423.5539.26231.405000220183
6527910017638876.16Moms Potato Salad232.92987.201863.36574.00413.20123.400000820183
652801001763824226.77Better Fancy Canned Sardines1431.2327297.5151524.2816188.9011108.61758.2641673620183
652811001763824479.26Imagine Popsicles1084.6127582.0252061.2814234.2213347.80574.6254174820183

Duplicate rows

Most frequently occurring

CustKeyDiscount AmountItemList PriceSales AmountSales Amount Based on List PriceSales Cost AmountSales Margin AmountSales PriceSales QuantityYearMonth# duplicates
309210025552601.9033Washington Apple Drink1419.5833817.681419.5833353.09464.59817.68120171021
85010013080634.6133Washington Apple Drink1419.5833784.971419.5833353.09431.88784.97120191020
85210013080634.6133Washington Apple Drink1419.5833784.971419.5833353.09431.88784.97120191220
309410025552601.9033Washington Apple Drink1419.5833817.681419.5833353.09464.59817.68120171220
4221000863824.8800High Top Walnuts622.0200597.14622.0200286.38310.76597.1412019616
85110013080634.6133Washington Apple Drink1419.5833784.971419.5833353.09431.88784.97120191116
2390100219860.0000High Top Walnuts622.0200622.02622.0200286.38335.64622.0212017615
4281000863824.8800High Top Walnuts622.0200597.14622.0200287.65309.49597.14120191214
85310013080634.6133Washington Apple Drink1419.5833784.971419.5833359.20425.77784.9712019314
85410013080634.6133Washington Apple Drink1419.5833784.971419.5833359.20425.77784.9712019914